Brain-computer Interface Based on Generation of Visual Images

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2011 Jun 23

PMID 21695206

Citations 20

Authors

Pavel Bobrov

Alexander Frolov

Charles Cantor

Irina Fedulova

Mikhail Bakhnyan

Alexander Zhavoronkov

Affiliations

Soon will be listed here.

Abstract

This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier.

Citing Articles

Evaluating the Feasibility of Visual Imagery for an EEG-Based Brain-Computer Interface.

Kilmarx J, Tashev I, Millan J, Sulzer J, Lewis-Peacock J IEEE Trans Neural Syst Rehabil Eng. 2024; 32:2209-2219.

PMID: 38843055 PMC: 11249027. DOI: 10.1109/TNSRE.2024.3410870.

Novel stability approach using Routh-Hurwitz criterion for brain computer interface applications.

Heo S, Choi H, Yang Y Technol Health Care. 2024; 32(S1):17-25.

PMID: 38669494 PMC: 11191471. DOI: 10.3233/THC-248002.

Measuring brain potentials of imagination linked to physiological needs and motivational states.

Proverbio A, Pischedda F Front Hum Neurosci. 2023; 17:1146789.

PMID: 37007683 PMC: 10050745. DOI: 10.3389/fnhum.2023.1146789.

Improving classification and reconstruction of imagined images from EEG signals.

Shimizu H, Srinivasan R PLoS One. 2022; 17(9):e0274847.

PMID: 36129927 PMC: 9491577. DOI: 10.1371/journal.pone.0274847.

Political Orientation as Psychological Defense or Basic Disposition? A Social Neuroscience Examination.

Nash K, Leota J Cogn Affect Behav Neurosci. 2021; 22(3):586-599.

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References

Klimesch W . EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev. 1999; 29(2-3):169-95. DOI: 10.1016/s0165-0173(98)00056-3. View

Grosse-Wentrup M, Buss M . Multiclass common spatial patterns and information theoretic feature extraction. IEEE Trans Biomed Eng. 2008; 55(8):1991-2000. DOI: 10.1109/TBME.2008.921154. View

Pfurtscheller G, Brunner C, Schlogl A, Lopes da Silva F . Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage. 2006; 31(1):153-9. DOI: 10.1016/j.neuroimage.2005.12.003. View

Boly M, Coleman M, Davis M, Hampshire A, Bor D, Moonen G . When thoughts become action: an fMRI paradigm to study volitional brain activity in non-communicative brain injured patients. Neuroimage. 2007; 36(3):979-92. DOI: 10.1016/j.neuroimage.2007.02.047. View

Nikolaev A, Ivanitskii G, Ivanitskii A . [Reproducible EEG alpha-rhythm patterns in solving psychological tasks]. Fiziol Cheloveka. 1998; 24(3):5-12. View

Pfurtscheller G, Neuper C, Flotzinger D, Pregenzer M . EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol. 1998; 103(6):642-51. DOI: 10.1016/s0013-4694(97)00080-1. View

Besserve M, Jerbi K, Laurent F, Baillet S, Martinerie J, Garnero L . Classification methods for ongoing EEG and MEG signals. Biol Res. 2008; 40(4):415-37. DOI: /S0716-97602007000500005. View

Berg P, Scherg M . Dipole modelling of eye activity and its application to the removal of eye artefacts from the EEG and MEG. Clin Phys Physiol Meas. 1991; 12 Suppl A:49-54. DOI: 10.1088/0143-0815/12/a/010. View

Wolpaw J, Birbaumer N, Heetderks W, McFarland D, Peckham P, Schalk G . Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng. 2000; 8(2):164-73. DOI: 10.1109/tre.2000.847807. View

10.

Millan J, Rupp R, Muller-Putz G, Murray-Smith R, Giugliemma C, Tangermann M . Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges. Front Neurosci. 2010; 4. PMC: 2944670. DOI: 10.3389/fnins.2010.00161. View

11.

Delorme A, Makeig S . EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004; 134(1):9-21. DOI: 10.1016/j.jneumeth.2003.10.009. View

12.

Fruitet J, McFarland D, Wolpaw J . A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain-computer interface. J Neural Eng. 2010; 7(1):16003. PMC: 3446205. DOI: 10.1088/1741-2560/7/1/016003. View

13.

Ishai A, Ungerleider L, Haxby J . Distributed neural systems for the generation of visual images. Neuron. 2001; 28(3):979-90. DOI: 10.1016/s0896-6273(00)00168-9. View

14.

Grech R, Cassar T, Muscat J, Camilleri K, Fabri S, Zervakis M . Review on solving the inverse problem in EEG source analysis. J Neuroeng Rehabil. 2008; 5:25. PMC: 2605581. DOI: 10.1186/1743-0003-5-25. View

15.

Kay K, Naselaris T, Prenger R, Gallant J . Identifying natural images from human brain activity. Nature. 2008; 452(7185):352-5. PMC: 3556484. DOI: 10.1038/nature06713. View

16.

Formaggio E, Storti S, Cerini R, Fiaschi A, Manganotti P . Brain oscillatory activity during motor imagery in EEG-fMRI coregistration. Magn Reson Imaging. 2010; 28(10):1403-12. DOI: 10.1016/j.mri.2010.06.030. View

17.

Allison B, Brunner C, Kaiser V, Muller-Putz G, Neuper C, Pfurtscheller G . Toward a hybrid brain-computer interface based on imagined movement and visual attention. J Neural Eng. 2010; 7(2):26007. DOI: 10.1088/1741-2560/7/2/026007. View

18.

Cerf M, Thiruvengadam N, Mormann F, Kraskov A, Quian Quiroga R, Koch C . On-line, voluntary control of human temporal lobe neurons. Nature. 2010; 467(7319):1104-8. PMC: 3010923. DOI: 10.1038/nature09510. View

19.

Kamitani Y, Tong F . Decoding the visual and subjective contents of the human brain. Nat Neurosci. 2005; 8(5):679-85. PMC: 1808230. DOI: 10.1038/nn1444. View

20.

Blankertz B, Dornhege G, Krauledat M, Muller K, Curio G . The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects. Neuroimage. 2007; 37(2):539-50. DOI: 10.1016/j.neuroimage.2007.01.051. View